Integration of Multimodal, Multiscale Imaging and Biomarker Data for Squamous Precancer Detection and Diagnosis

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dc.contributor.advisorRichards-Kortum, Rebecca
dc.creatorBrenes, David Roberto
dc.date.accessioned2023-08-09T15:21:35Z
dc.date.created2023-05
dc.date.issued2023-04-17
dc.date.submittedMay 2023
dc.date.updated2023-08-09T15:21:35Z
dc.descriptionEMBARGO NOTE: This item is embargoed until 2025-05-01
dc.description.abstractClinical experts play a crucial role in screening and diagnosing squamous precancers as they integrate and interpret increasing amounts of multiscale and multimodal medical data. For example, detecting precancerous lesions may involve the use of widefield imaging modalities, with a large field of view and modest spatial resolution. In contrast, histologic diagnosis of precancer may involve modalities with high spatial resolution and small field of view, such as light microscopy. Additionally, newly available molecular tests can provide valuable patient-specific information that can help confirm a diagnosis or tailor a treatment to an individual patient. Due to the complexity and ever-growing amount of information available, efforts are underway to develop computer-aided diagnostic (CAD) systems to aid clinicians in the interpretation of medical data. However, most proposed CAD systems focus on applications involving a single modality or multiple modalities with similar spatial scales. The work presented in this thesis aimed to develop novel approaches to integrate multimodal, multiscale imaging and biomarker data for squamous precancer detection and diagnosis. Specifically, this thesis describes work to develop: (1) a deep learning model to diagnose high-grade squamous precancers using high-resolution endomicroscopy images, with performance validation across multiple anatomical sites, (2) a multiscale optical imaging fusion network that integrated high-resolution endomicroscopy images and widefield colposcopy data to diagnose cervical precancers, with validation in a large study in Brazil, and (3) a fusion and analysis framework integrating optical imaging data with DNA molecular test results and gene expression profiling for more accurate precancer diagnosis and prediction of the risk of lesion progression.
dc.embargo.lift2025-05-01
dc.embargo.terms2025-05-01
dc.format.mimetypeapplication/pdf
dc.identifier.citationBrenes, David Roberto. "Integration of Multimodal, Multiscale Imaging and Biomarker Data for Squamous Precancer Detection and Diagnosis." (2023) Diss., Rice University. <a href="https://hdl.handle.net/1911/115088">https://hdl.handle.net/1911/115088</a>.
dc.identifier.urihttps://hdl.handle.net/1911/115088
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectfusion
dc.subjectintegration
dc.subjectmultimodal
dc.subjectmultiscale imaging
dc.subjectbiomarker
dc.subjectprecancer
dc.subjectdeep learning
dc.subjectdetection
dc.subjectdiagnosis
dc.subjectmedical imaging
dc.subjectHPV
dc.titleIntegration of Multimodal, Multiscale Imaging and Biomarker Data for Squamous Precancer Detection and Diagnosis
dc.typeThesis
dc.type.materialText
thesis.degree.departmentBioengineering
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
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